This fast-track proposal applies advanced kinetic image pattern recognition (KIPR) technologies to predict induced pluripotent stem cell (iPSC) reprogramming colonies'differentiation outcomes for significantly improved yield and robustness of differentiation protocols. The objectives of the proposed tool are 1) Teaching: creation of scores for induced colony differentiation outcome prediction by machine learning;2) Reprogramming: optimal reprogramming harvest time determination by continuous colony score monitoring;3) Differentiation: selection of colonies with the highest prediction scores for differentiation at the reprogramming harvest time;4) Differentiation: cell cluster quality control by continuous monitoring during differentiation. The specific aims of this fast-track proposal are Phase I: 1) Extend SVCell for the prediction of induced colony differentiation outcomes;2) Validate that prediction of colony differentiation outcomes can improve the yield of CM differentiation. Phase II: 1) Validate that the integrated system can be taught to be robust and high yielding for a diverse set of human fibroblast input samples and different reprogramming / differentiation protocols;2) Integrate SVCell with a state-of-the-art continuous cell imaging and culture system to create a prototype patient-specific cell generation system;3) Validate the integrated system as a patient-specific cell generation product. The ultimate goal of this fast-track proposal is to develop and validate an image-guided efficient patient-specific cardiomyocyte generation system. This will be achieved by integrating our established SVCell software containing advanced KIPR technologies with a live cell imaging technology to synthesize state-of-the-art cell fate control protocols against iPSC. Patient-specific cell generation systems could "personalize" medicine by reprogramming patient-specific cells and directing their differentiation to specific lineages (e.g. heart, brain) for disease diagnosis and personalized drug testing. Successful development of the patient-specific cell generation system of this proposal could catalyze personalized medicine and revolutionize health care in both diagnosis and therapy. PUBLIC HEALTH RELEVANCE: Image-guided efficient patient-specific cell generation systems could "personalize" medicine by reprogramming patient-specific cells and directing their differentiation to specific lineages (e.g. heart, brain) for disease diagnosis and personalized drug testing. This could catalyze personalized medicine and revolutionize health care in both diagnosis and therapy.